Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies

Andrew Moriarty; Lewis Paton; Kym IE Snell; Lucinda Archer; Richard D Riley; Joshua E J Buckman; Carolyn A Chew-Graham; Simon Gilbody; Shehzad Ali; Stephen Pilling; Nick Meader; Bob Phillips; Peter A Coventry; Jaime Delgadillo; David Richards; Chris Salisbury; Dean McMillan

doi:10.1136/bmjment-2024-301226

Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies

Andrew Moriarty^*
, Lewis Paton
, Kym IE Snell
, Lucinda Archer
, Richard D Riley
, Joshua E J Buckman
, Carolyn A Chew-Graham
, Simon Gilbody
, Shehzad Ali
, Stephen Pilling
, Nick Meader
, Bob Phillips
, Peter A Coventry
, Jaime Delgadillo
, David Richards
, Chris Salisbury
, Dean McMillan

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

3 Citations (Scopus)

Abstract

Background Relapse of depression is common and contributes to the overall associated morbidity and burden. We lack evidence-based tools to estimate an individual’s risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention.

Objective The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care.

Methods Multilevel logistic regression models were developed, using individual participant data from seven primary care-based studies (n=1244), to predict relapse of depression. The model was internally validated using bootstrapping, and generalisability was explored using internal–external cross-validation.

Findings Residual depressive symptoms (OR: 1.13 (95% CI: 1.07 to 1.20), p<0.001) and baseline depression severity (OR: 1.07 (1.04 to 1.11), p<0.001) were associated with relapse. The validated model had low discrimination (C-statistic 0.60 (0.55–0.65)) and miscalibration concerns (calibration slope 0.81 (0.31–1.31)). On secondary analysis, being in a relationship was associated with reduced risk of relapse (OR: 0.43 (0.28–0.67), p<0.001); this remained statistically significant after correction for multiple significance testing.

Conclusions We could not predict risk of depression relapse with sufficient accuracy in primary care data, using routinely recorded measures. Relationship status warrants further research to explore its role as a prognostic factor for relapse.

Clinical implications Until we can accurately stratify patients according to risk of relapse, a universal approach to relapse prevention may be most beneficial, either during acute-phase treatment or post remission. Where possible, this could be guided by the presence or absence of known prognostic factors (eg, residual depressive symptoms) and targeted towards these.

Trial registration number NCT04666662.

Original language	English
Article number	e301226
Number of pages	8
Journal	BMJ Mental Health
Volume	27
Issue number	1
DOIs	https://doi.org/10.1136/bmjment-2024-301226
Publication status	Published - 28 Oct 2024

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© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. Published by BMJ.

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Moriarty, A., Paton, L., Snell, K. IE., Archer, L., Riley, R. D., Buckman, J. E. J., Chew-Graham, C. A., Gilbody, S., Ali , S., Pilling, S., Meader, N., Phillips, B., Coventry, P. A., Delgadillo, J., Richards, D., Salisbury, C., & McMillan, D. (2024). Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies. BMJ Mental Health, 27(1), Article e301226. https://doi.org/10.1136/bmjment-2024-301226

@article{d386622a05444e7b8b9a59bb1bb00834,

title = "Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies",

abstract = "Background Relapse of depression is common and contributes to the overall associated morbidity and burden. We lack evidence-based tools to estimate an individual{\textquoteright}s risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention. Objective The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care. Methods Multilevel logistic regression models were developed, using individual participant data from seven primary care-based studies (n=1244), to predict relapse of depression. The model was internally validated using bootstrapping, and generalisability was explored using internal–external cross-validation. Findings Residual depressive symptoms (OR: 1.13 (95\% CI: 1.07 to 1.20), p<0.001) and baseline depression severity (OR: 1.07 (1.04 to 1.11), p<0.001) were associated with relapse. The validated model had low discrimination (C-statistic 0.60 (0.55–0.65)) and miscalibration concerns (calibration slope 0.81 (0.31–1.31)). On secondary analysis, being in a relationship was associated with reduced risk of relapse (OR: 0.43 (0.28–0.67), p<0.001); this remained statistically significant after correction for multiple significance testing. Conclusions We could not predict risk of depression relapse with sufficient accuracy in primary care data, using routinely recorded measures. Relationship status warrants further research to explore its role as a prognostic factor for relapse. Clinical implications Until we can accurately stratify patients according to risk of relapse, a universal approach to relapse prevention may be most beneficial, either during acute-phase treatment or post remission. Where possible, this could be guided by the presence or absence of known prognostic factors (eg, residual depressive symptoms) and targeted towards these. Trial registration number NCT04666662.",

author = "Andrew Moriarty and Lewis Paton and Snell, \{Kym IE\} and Lucinda Archer and Riley, \{Richard D\} and Buckman, \{Joshua E J\} and Chew-Graham, \{Carolyn A\} and Simon Gilbody and Shehzad Ali and Stephen Pilling and Nick Meader and Bob Phillips and Coventry, \{Peter A\} and Jaime Delgadillo and David Richards and Chris Salisbury and Dean McMillan",

note = "Publisher Copyright: {\textcopyright} Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. Published by BMJ.",

year = "2024",

month = oct,

day = "28",

doi = "10.1136/bmjment-2024-301226",

language = "English",

volume = "27",

journal = "BMJ Mental Health",

issn = "2755-9734",

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Moriarty, A, Paton, L, Snell, KIE, Archer, L, Riley, RD, Buckman, JEJ, Chew-Graham, CA, Gilbody, S, Ali , S, Pilling, S, Meader, N, Phillips, B, Coventry, PA, Delgadillo, J, Richards, D, Salisbury, C & McMillan, D 2024, 'Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies', BMJ Mental Health, vol. 27, no. 1, e301226. https://doi.org/10.1136/bmjment-2024-301226

Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies. / Moriarty, Andrew; Paton, Lewis; Snell, Kym IE et al.
In: BMJ Mental Health, Vol. 27, No. 1, e301226, 28.10.2024.

Research output: Contribution to journal › Article (Academic Journal) › peer-review

TY - JOUR

T1 - Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care

T2 - secondary analysis of pooled individual participant data from multiple studies

AU - Moriarty, Andrew

AU - Paton, Lewis

AU - Snell, Kym IE

AU - Archer, Lucinda

AU - Riley, Richard D

AU - Buckman, Joshua E J

AU - Chew-Graham, Carolyn A

AU - Gilbody, Simon

AU - Ali , Shehzad

AU - Pilling, Stephen

AU - Meader, Nick

AU - Phillips, Bob

AU - Coventry, Peter A

AU - Delgadillo, Jaime

AU - Richards, David

AU - Salisbury, Chris

AU - McMillan, Dean

PY - 2024/10/28

Y1 - 2024/10/28

N2 - Background Relapse of depression is common and contributes to the overall associated morbidity and burden. We lack evidence-based tools to estimate an individual’s risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention. Objective The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care. Methods Multilevel logistic regression models were developed, using individual participant data from seven primary care-based studies (n=1244), to predict relapse of depression. The model was internally validated using bootstrapping, and generalisability was explored using internal–external cross-validation. Findings Residual depressive symptoms (OR: 1.13 (95% CI: 1.07 to 1.20), p<0.001) and baseline depression severity (OR: 1.07 (1.04 to 1.11), p<0.001) were associated with relapse. The validated model had low discrimination (C-statistic 0.60 (0.55–0.65)) and miscalibration concerns (calibration slope 0.81 (0.31–1.31)). On secondary analysis, being in a relationship was associated with reduced risk of relapse (OR: 0.43 (0.28–0.67), p<0.001); this remained statistically significant after correction for multiple significance testing. Conclusions We could not predict risk of depression relapse with sufficient accuracy in primary care data, using routinely recorded measures. Relationship status warrants further research to explore its role as a prognostic factor for relapse. Clinical implications Until we can accurately stratify patients according to risk of relapse, a universal approach to relapse prevention may be most beneficial, either during acute-phase treatment or post remission. Where possible, this could be guided by the presence or absence of known prognostic factors (eg, residual depressive symptoms) and targeted towards these. Trial registration number NCT04666662.

AB - Background Relapse of depression is common and contributes to the overall associated morbidity and burden. We lack evidence-based tools to estimate an individual’s risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention. Objective The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care. Methods Multilevel logistic regression models were developed, using individual participant data from seven primary care-based studies (n=1244), to predict relapse of depression. The model was internally validated using bootstrapping, and generalisability was explored using internal–external cross-validation. Findings Residual depressive symptoms (OR: 1.13 (95% CI: 1.07 to 1.20), p<0.001) and baseline depression severity (OR: 1.07 (1.04 to 1.11), p<0.001) were associated with relapse. The validated model had low discrimination (C-statistic 0.60 (0.55–0.65)) and miscalibration concerns (calibration slope 0.81 (0.31–1.31)). On secondary analysis, being in a relationship was associated with reduced risk of relapse (OR: 0.43 (0.28–0.67), p<0.001); this remained statistically significant after correction for multiple significance testing. Conclusions We could not predict risk of depression relapse with sufficient accuracy in primary care data, using routinely recorded measures. Relationship status warrants further research to explore its role as a prognostic factor for relapse. Clinical implications Until we can accurately stratify patients according to risk of relapse, a universal approach to relapse prevention may be most beneficial, either during acute-phase treatment or post remission. Where possible, this could be guided by the presence or absence of known prognostic factors (eg, residual depressive symptoms) and targeted towards these. Trial registration number NCT04666662.

U2 - 10.1136/bmjment-2024-301226

DO - 10.1136/bmjment-2024-301226

M3 - Article (Academic Journal)

C2 - 39467616

SN - 2755-9734

VL - 27

JO - BMJ Mental Health

JF - BMJ Mental Health

IS - 1

M1 - e301226

ER -

Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies

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